Rancang Bangun Aplikasi Deteksi Penyakit Tanaman Jagung Melalui Citra Daun Berbasis Android Menggunakan Algoritma Convolutional Neural Network
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Abstract
Corn is a staple product that is important to Indonesia's development and economy. Corn disease is a major source of yield loss and lower earnings in the corn industry. Each disease demands a distinct intervention, but not all farmers know the type of disease that allows for mistakes in disease management. Convolutional Neural Network (CNN) and transfer learning can provide the best way for recognizing corn leaf disease, as both methods are known to classify objects with high accuracy. Transfer learning was chosen because it allows features and weights from prior training sessions to be reused. As a result, computation time can be reduced while accuracy is raised. In this study, an android app was developed using the CNN pre-trained models to identify corn leaf disease. The dataset includes 2600 images of corn leaf samples from three categories (Bacterial Leaf Blight, Common Rust, and healthy leaves). The model used is MobileNetV2. From the experimental results, the MobileNetV2 produced accuracy of 99% for Bacterial Leaf Blight, 100 % for Common Rust, and 100% for healthy leaves. Performance efficiency testing showed that the highest CPU and memory usage were 15.8% and 178 MB, respectively. This app is compatible with all Android versions, from 8.0 (Oreo) to 13 (Tiramisu).
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